What is the comparative health status and associated risk factors for the Métis? A population-based study in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: Métis are descendants of early 17th century relationships between North American Indians and Europeans. This study's objectives were: (1) to compare the health status of the Métis people to all other residents of Manitoba, Canada; and (2) to analyze factors in predicting the likelihood of diabetes and related lower limb amputation. METHODS: Using de-identified administrative databases plus the Métis Population Database housed at the Manitoba Centre for Health Policy, age/sex-adjusted rates of mortality and disease were calculated for Métis (n = 73,016) and all other Manitobans (n = 1,104,672). Diseases included: hypertension, arthritis, diabetes, ischemic heart disease (age 19+); osteoporosis (age 50+); acute myocardial infarction (AMI) and stroke (age 40+); total respiratory morbidity (TRM, all ages). Using logistic regression, predictors of diabetes (2004/05-2006/07) and diabetes-related lower-limb amputations (2002/03-2006/07) were analyzed. RESULTS: Disease rates were higher for Métis compared to all others: premature mortality before age 75 (4.0 vs. 3.3 per 1000, p < .001); total mortality (9.7 vs. 8.4 per 1000, p < .001); injury mortality (0.58 vs. 0.51 per 1000, p < .03); Potential Years of Life Lost (64.6 vs. 54.6 per 1000, p < .001); all-cause 5-year mortality for people with diabetes (20.8% vs. 18.6%, p < .02); hypertension (27.9% vs. 24.8%, p < .001); arthritis (24.2% vs. 19.9%, p < .001), TRM (13.6% vs. 10.6%, p < .001); diabetes (11.8% vs. 8.8%, p < .001); diabetes-related lower limb amputation (24.1 vs. 16.2 per 1000, p < .001); ischemic heart disease (12.2% vs. 8.7%, p < .001); osteoporosis (12.2% vs. 12.3%, NS), dialysis initiation (0.46% vs. 0.34%, p < .001); AMI (5.4 vs. 4.3 per 1000, p < .001); stroke (3.6 vs. 2.9 per 1000, p < .001). Controlling for geography, age, sex, income, continuity of care and comorbidities, Métis were more likely to have diabetes (aOR = 1.29, 95% CI 1.25-1.34), but not diabetes-related lower limb amputation (aOR = 1.13, 95% CI 0.90-1.40, NS). Continuity of care was associated with decreased risk of amputation both provincially (aOR = 0.71, 95% CI 0.62-0.81) and for Métis alone (aOR = 0.62, 95% CI 0.40-0.96). CONCLUSION: Despite universal healthcare, Métis' illness and mortality rates are mostly higher. Although elevated diabetes risk persists for the Métis even after adjusting for sociodemographic, healthcare and comorbidity variables, the risk of amputation for Métis appears more related to healthcare access rather than ethnicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".